A method of rate control of a display device includes receiving compressed stress data for a slice of a display, decompressing the compressed stress data to obtain reconstructed stress data for the slice, adding additional stress data to the reconstructed stress data to obtain updated stress data for the slice, encoding the updated stress data at a first precision level (pc) to generate first updated compressed stress data for the slice, in response to a size (bc) of the first updated compressed stress data for the slice of the display exceeding a size (bt) of a buffer, determining a second precision level (p) based on the first precision level (pc), a third precision level (ps) of the additional stress data, and a fourth precision level (pb) of the buffer, and encoding the updated stress data at the second precision level (p) to generate second updated compressed stress data.
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2. The method of claim 1, wherein determining the second precision level (p) comprises setting the second precision level (p) to be equal to [(pc−pm)bt/bc]+pm, wherein pm is a minimum of the third precision level (ps) and the fourth precision level (pb).
This invention relates to a method for dynamically adjusting precision levels in computational systems, particularly for optimizing performance and resource usage in numerical computations. The method addresses the challenge of balancing computational accuracy with efficiency, where fixed precision levels may either waste resources or compromise accuracy. The invention dynamically determines a second precision level (p) based on a calculated relationship between a third precision level (ps) and a fourth precision level (pb), which are derived from system constraints or user requirements. The second precision level (p) is set to a value derived from the formula [(pc−pm)bt/bc]+pm, where pm is the minimum of ps and pb. This ensures that the precision level is adjusted in a way that maintains computational accuracy while optimizing resource usage. The method involves calculating intermediate values (pc, pm, bt, bc) to dynamically adjust the precision level, ensuring that the system adapts to varying computational demands without manual intervention. The invention is particularly useful in applications requiring real-time adjustments, such as scientific simulations, financial modeling, or machine learning, where both accuracy and efficiency are critical. By dynamically setting the precision level, the method ensures that computations are performed with the necessary accuracy while minimizing unnecessary resource consumption.
4. The method of claim 1, wherein determining the second precision level (p) comprises setting the second precision level (p) to be equal to pc bt/bc.
This invention relates to a method for dynamically adjusting precision levels in computational systems, particularly for optimizing performance and resource usage in numerical computations. The method addresses the problem of inefficient resource allocation in systems where fixed precision levels lead to either excessive computational overhead or insufficient accuracy. By dynamically adjusting precision levels based on system parameters, the method improves efficiency while maintaining required accuracy. The method involves determining a second precision level (p) for a computational process. This second precision level is calculated by setting it equal to the product of a base precision level (pc) and a ratio of a target bit length (bt) to a current bit length (bc). The base precision level (pc) represents a reference precision value, while the target bit length (bt) and current bit length (bc) define the desired and actual bit lengths for the computation, respectively. This adjustment ensures that the precision level scales appropriately with the bit length, optimizing computational resources without compromising accuracy. The method may be applied in various computational contexts, such as numerical simulations, data processing, or machine learning, where precision requirements vary dynamically. By dynamically adjusting the precision level, the method reduces unnecessary computational overhead while ensuring that the precision remains sufficient for the task at hand. This approach is particularly useful in systems with limited processing power or where energy efficiency is a priority.
5. The method of claim 1, wherein the first precision level (pc) is a precision level used to generate the compressed stress data.
This invention relates to data compression techniques for stress data, particularly in engineering or scientific applications where stress measurements are collected and stored. The problem addressed is the need to efficiently compress stress data while maintaining sufficient precision for accurate analysis. Stress data often requires high precision to ensure reliability in simulations, structural analysis, or material testing, but storing uncompressed high-precision data consumes significant memory and processing resources. The invention describes a method for compressing stress data using a first precision level (pc) to generate the compressed data. The first precision level (pc) defines the resolution or accuracy at which the stress values are quantized or encoded during compression. By selecting an appropriate precision level, the method balances the trade-off between data size reduction and the retention of critical stress information. The method may involve quantizing stress values into discrete levels based on the first precision level (pc), then encoding these quantized values using lossless or lossy compression techniques. The compressed stress data can later be decompressed and reconstructed with the original precision level (pc) for further analysis. The invention may also include additional steps such as preprocessing the stress data to normalize or scale values before compression, or using adaptive precision levels that vary based on the stress magnitude or spatial/temporal characteristics of the data. The method ensures that the compressed stress data remains usable for engineering applications while reducing storage and transmission requirements.
6. The method of claim 1, further comprising adding dither, in addition to the additional stress data, to the reconstructed stress data to obtain the updated stress data for the slice.
This invention relates to stress data reconstruction in computational simulations, particularly for improving accuracy in stress analysis of materials or structures. The core method involves reconstructing stress data for a slice of a material or structure by combining original stress data with additional stress data derived from a stress model. The additional stress data compensates for inaccuracies or missing information in the original stress data, enhancing the fidelity of the reconstructed stress data. The updated method further includes adding dither to the reconstructed stress data. Dither is a controlled noise or perturbation applied to the data to reduce artifacts, such as banding or quantization errors, that may arise during reconstruction. By incorporating dither, the method ensures smoother transitions and more uniform stress distribution in the final stress data for the slice. This step is particularly useful in simulations where stress gradients are critical, such as in material fatigue analysis or structural integrity assessments. The combination of additional stress data and dither improves both the accuracy and robustness of the stress reconstruction process.
9. The display device of claim 8, wherein the processor is further configured to determine the second precision level (p) by setting the second precision level (p) to be equal to [(pc−pm)bt/bc]+pm, wherein pm is a minimum of the third precision level (ps) and the fourth precision level (pb).
This invention relates to display devices with adaptive precision control for image processing. The problem addressed is optimizing computational efficiency and visual quality by dynamically adjusting precision levels for different image regions based on their characteristics. The display device includes a processor that analyzes image data to determine precision requirements for different regions. The processor assigns a first precision level to a first region of the image data based on a first characteristic, and a second precision level to a second region based on a second characteristic. The second precision level is calculated using a formula that balances the precision of the first region (pc) and a minimum precision level (pm), which is the lower of two other precision levels (ps and pb). The formula [(pc−pm)bt/bc]+pm ensures smooth transitions between regions while maintaining computational efficiency. The processor then processes the image data using these adaptive precision levels before displaying it. This approach reduces processing overhead for less critical regions while preserving quality in important areas, improving overall system performance without sacrificing visual fidelity. The invention is particularly useful in high-resolution displays and real-time rendering applications where precision allocation must be dynamically optimized.
11. The display device of claim 8, wherein the processor is further configured to determine the second precision level (p) by setting the second precision level (p) to be equal to pc bt/bc.
A display device includes a processor configured to adjust the precision level of image data for display. The device addresses the challenge of optimizing image quality and processing efficiency by dynamically adjusting precision levels based on display characteristics. The processor determines a second precision level (p) by calculating it as the product of a base precision level (pc) and the ratio of a target bit depth (bt) to a current bit depth (bc). This allows the device to scale precision dynamically, ensuring that image data is processed at an appropriate level of detail for the display's capabilities. The processor may also adjust the precision level of a first image data set based on a first precision level (p1) and a second image data set based on the second precision level (p2). The device further includes a display panel for rendering the processed image data. The dynamic precision adjustment helps balance computational load and visual fidelity, particularly in high-resolution or high-dynamic-range displays where precision requirements vary. The invention improves efficiency in image processing pipelines by avoiding unnecessary high-precision calculations when lower precision suffices for the display's output.
12. The display device of claim 8, wherein the first precision level (pc) is a precision level used to generate the compressed stress data stored in the buffer.
A display device includes a stress detection unit that measures stress data from a display panel, a compression unit that compresses the stress data into compressed stress data, and a buffer that stores the compressed stress data. The compression unit generates the compressed stress data at a first precision level, which is the precision level used for storing the compressed stress data in the buffer. The stress detection unit may include a plurality of stress sensors distributed across the display panel to measure stress data at multiple locations. The compression unit may use a lossy or lossless compression algorithm to reduce the data size while maintaining the first precision level. The buffer may be a memory module or a storage device that temporarily or permanently stores the compressed stress data for further processing or analysis. The display device may also include a decompression unit that reconstructs the stress data from the compressed stress data stored in the buffer, allowing for stress monitoring and compensation to improve display performance and longevity. The first precision level ensures that the compressed stress data retains sufficient accuracy for reliable stress analysis and compensation.
13. The display device of claim 8, further comprising a dithering circuit configured to add dither, in addition to the additional stress data, to the reconstructed stress data to obtain the updated stress data for the slice.
This invention relates to display devices, specifically those that reconstruct and process stress data for display slices to improve image quality. The problem addressed is the need to enhance visual fidelity by compensating for stress-induced distortions in display panels, particularly in high-resolution or flexible displays where stress variations can degrade image accuracy. The display device includes a stress data reconstruction circuit that processes stress data for each slice of the display to generate reconstructed stress data. This circuit compensates for stress-induced distortions by adjusting pixel values based on the stress data, ensuring uniform brightness and color accuracy across the display. Additionally, the device includes a stress data update circuit that modifies the reconstructed stress data by incorporating additional stress data, such as environmental or operational stress factors, to further refine the compensation process. To enhance the visual quality, the device also includes a dithering circuit that applies dithering to the updated stress data. Dithering introduces controlled noise or variations to the stress-compensated pixel values, reducing visible artifacts like banding or quantization errors. This step ensures smoother gradients and improved perceptual quality in the displayed image. The combined use of stress data reconstruction, additional stress compensation, and dithering allows the display to maintain high fidelity under varying stress conditions, making it suitable for applications where display performance is critical.
14. The display device of claim 8, further comprising a memory controller configured to store the second updated compressed stress data in the buffer.
A display device includes a stress detection module that monitors stress levels of display components, such as pixels or drivers, during operation. The device generates stress data representing the monitored stress levels and compresses this data to reduce storage requirements. A stress data processor updates the compressed stress data based on new stress measurements, generating a first updated compressed stress data. The processor further compresses this updated data to produce a second updated compressed stress data, which is then stored in a buffer. The buffer temporarily holds the compressed data for subsequent analysis or processing. The memory controller manages the storage operations, ensuring efficient use of memory resources. This system helps optimize display performance by tracking and managing component stress, preventing degradation, and extending the lifespan of the display. The compression techniques reduce the data size, allowing for faster processing and lower memory usage. The buffer acts as an intermediate storage, facilitating real-time or near-real-time stress monitoring and adjustment. This approach is particularly useful in high-resolution or high-dynamic-range displays where stress monitoring is critical for maintaining image quality and reliability.
16. The non-transitory computer readable medium of claim 15, wherein the computer code, when executed on the processor, determines the second precision level (p) by setting the second precision level (p) to be equal to [(pc−pm)bt/bc]+pm, wherein pm is a minimum of the third precision level (ps) and the fourth precision level (pb).
This invention relates to a computer-implemented method for dynamically adjusting precision levels in numerical computations to optimize performance and accuracy. The problem addressed is the trade-off between computational efficiency and numerical precision, particularly in systems where different operations require varying levels of precision. The solution involves dynamically determining a second precision level (p) based on a minimum precision level (pm) derived from a third precision level (ps) and a fourth precision level (pb). The second precision level (p) is calculated using the formula [(pc−pm)bt/bc]+pm, where pc is a base precision level, bt is a target bit length, and bc is a base bit length. This approach ensures that the precision level is adjusted according to the minimum required precision of the involved operations, balancing computational efficiency with accuracy. The method is implemented via computer code stored on a non-transitory computer-readable medium and executed by a processor. The system includes a processor, memory, and input/output interfaces to facilitate the dynamic precision adjustment. The invention is particularly useful in high-performance computing, scientific simulations, and real-time data processing where precision requirements vary across different computational tasks.
18. The non-transitory computer readable medium of claim 15, wherein the first precision level (pc) is a precision level used to generate the compressed stress data.
The invention relates to data compression techniques for stress data, particularly in computational simulations or engineering applications where stress values are processed. The problem addressed is the need to efficiently compress stress data while maintaining accuracy and reducing computational overhead. Stress data often involves high-dimensional numerical values that require significant storage and processing resources, making compression essential for practical applications. The invention provides a method for compressing stress data using a hierarchical precision approach. A first precision level (pc) is used to generate the compressed stress data, where this precision level determines the resolution or accuracy of the compressed representation. The method involves selecting a precision level that balances compression efficiency and data fidelity, ensuring that the compressed data retains sufficient accuracy for subsequent analysis or simulation. The compressed stress data can then be decompressed or further processed while maintaining the desired precision. The invention may also include additional techniques for optimizing the compression process, such as adaptive precision adjustment based on stress value distributions or error thresholds. The hierarchical precision approach allows for scalable compression, where different precision levels can be applied to different portions of the stress data to optimize storage and processing efficiency. This method is particularly useful in finite element analysis, structural simulations, or other computational mechanics applications where stress data is critical but resource-intensive.
19. The non-transitory computer readable medium of claim 15, wherein the computer code, when executed on the processor, further implements the method by adding dither, in addition to the additional stress data, to the reconstructed stress data to obtain the updated stress data for the slice.
This invention relates to stress data processing in computational simulations, particularly for improving the accuracy and realism of stress distribution models in engineering or scientific applications. The problem addressed is the need to enhance the fidelity of reconstructed stress data, which may be derived from incomplete or noisy measurements or simulations, by incorporating additional stress data and controlled perturbations to better represent real-world variability. The method involves a computational process where stress data for a specific slice or section of a material or structure is reconstructed from a set of input data. To refine this reconstruction, additional stress data is integrated into the existing stress data for the slice. Furthermore, dither—a form of controlled noise or random variation—is applied to the reconstructed stress data, in addition to the added stress data, to produce updated stress data. This dithering step introduces controlled variability, helping to mitigate artifacts or biases in the reconstruction process and improving the overall accuracy of the stress distribution model. The updated stress data, now incorporating both the additional stress data and the dither, provides a more realistic representation of the stress conditions in the slice. This approach is particularly useful in applications where precise stress modeling is critical, such as material testing, structural analysis, or finite element simulations. The method is implemented via computer-executable code stored on a non-transitory computer-readable medium, ensuring reproducibility and scalability across different computational platforms.
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May 27, 2021
April 9, 2024
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